Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2004.08.010 Byline: Sancho Salcedo-Sanz (a), Yong Xu (b), Xin Yao (b) Abstract: In this paper we tackle the task assignment problem (TSAP) in heterogeneous computer systems. The TSAP consists of assigning a given distributed computer program formed by a number of tasks to a number of processors, subject to a set of constraints, and in such a way a given cost function to be minimized. We introduce a novel formulation of the problem, in which each processor is limited in the number of task it can handle, due to the so called resource constraint. We propose two hybrid meta-heuristic approaches for solving this problem. Both hybrid approaches use a Hopfield neural network to solve the problem's constraints, mixed with a genetic algorithm (GA) and a simulated annealing for improving the quality of the solutions found. We test the performance of the proposed algorithms in several computational TSAP instances, using a GA with a penalty function and a GA with a repairing heuristic for comparison purposes. We will show that both meta-heuristics approaches are very good approaches for solving the TSAP. Author Affiliation: (a) Department of Signal Theory and Communications, Universidad Carlos III de, 28911 Leganes, Madrid, Spain (b) Centre for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, The University of Birmingham